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Does pytorch support repeating a tensor without allocating significantly more memory?
Assume we have a tensor
t = torch.ones((1,1000,1000))
t10 = t.repeat(10,1,1)
Repeating t 10 times will require take 10x the memory. Is there a way how I can create a tensor t10 without allocating significantly more memory?
Here is a related question, but without answers.
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You can use following Einops function. Where b is the number of times you want your tensor to be repeated and h , w the additional dimensions to the tensor. Show activity on this post. Repeated values are memory heavy, in most cases best practice is to use broadcasting.
Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the stride to 0.
flatten. Flattens input by reshaping it into a one-dimensional tensor. If start_dim or end_dim are passed, only dimensions starting with start_dim and ending with end_dim are flattened.
You can use torch.expand
t = torch.ones((1, 1000, 1000))
t10 = t.expand(10, 1000, 1000)
Keep in mind that the t10 is just a reference to t. So for example, a change to t10[0,0,0] will result in the same change in t[0,0,0] and every member of t10[:,0,0].
Other than direct access, most operations performed on t10 will cause memory to be copied which will break the reference and cause more memory to be used. For example: changing the device (.cpu(), .to(device=...), .cuda()), changing the datatype (.float(), .long(), .to(dtype=...)), or using .contiguous().
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